Overview

Dataset statistics

Number of variables22
Number of observations1000
Missing cells1500
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory172.0 KiB
Average record size in memory176.1 B

Variable types

Numeric18
Categorical3
Unsupported1

Alerts

F3 is highly overall correlated with F14High correlation
F14 is highly overall correlated with F1High correlation
F19 is highly overall correlated with F21High correlation
F20 is highly overall correlated with F19High correlation
F1 is highly overall correlated with F14High correlation
F7 is highly overall correlated with F15High correlation
F15 is highly overall correlated with F7High correlation
F21 is highly overall correlated with F19High correlation
F21 has 500 (50.0%) missing valuesMissing
Class has 1000 (100.0%) missing valuesMissing
F16 is uniformly distributedUniform
Class is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-01-11 12:07:26.981123
Analysis finished2023-01-11 12:07:46.849917
Duration19.87 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

F1
Real number (ℝ)

Distinct950
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0485561
Minimum0.11197
Maximum4.657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:07:46.896535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.11197
5-th percentile0.159345
Q10.380375
median0.7648
Q31.39
95-th percentile2.9931
Maximum4.657
Range4.54503
Interquartile range (IQR)1.009625

Descriptive statistics

Standard deviation0.9029195
Coefficient of variation (CV)0.86110749
Kurtosis2.2392235
Mean1.0485561
Median Absolute Deviation (MAD)0.44285
Skewness1.5336633
Sum1048.5561
Variance0.81526363
MonotonicityNot monotonic
2023-01-11T12:07:46.954191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3578 2
 
0.2%
1.065 2
 
0.2%
1.064 2
 
0.2%
0.19 2
 
0.2%
3.551 2
 
0.2%
1.574 2
 
0.2%
2.253 2
 
0.2%
1.135 2
 
0.2%
1.283 2
 
0.2%
2.368 2
 
0.2%
Other values (940) 980
98.0%
ValueCountFrequency (%)
0.11197 1
0.1%
0.11271 1
0.1%
0.11508 1
0.1%
0.11533 1
0.1%
0.11618 1
0.1%
0.11649 1
0.1%
0.1178 1
0.1%
0.11839 1
0.1%
0.11847 1
0.1%
0.1189 1
0.1%
ValueCountFrequency (%)
4.657 1
0.1%
4.571 1
0.1%
4.564 1
0.1%
4.552 1
0.1%
4.515 1
0.1%
4.417 1
0.1%
4.386 1
0.1%
4.366 1
0.1%
4.33 1
0.1%
4.262 1
0.1%

F2
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
511 
0
489 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 511
51.1%
0 489
48.9%

Length

2023-01-11T12:07:47.002095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T12:07:47.048702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 511
51.1%
0 489
48.9%

Most occurring characters

ValueCountFrequency (%)
1 511
51.1%
0 489
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 511
51.1%
0 489
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 511
51.1%
0 489
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 511
51.1%
0 489
48.9%

F3
Real number (ℝ)

Distinct980
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5340.3338
Minimum-18619.44
Maximum394.56
Zeros0
Zeros (%)0.0%
Negative999
Negative (%)99.9%
Memory size7.9 KiB
2023-01-11T12:07:47.092925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-18619.44
5-th percentile-7766.74
Q1-5465.39
median-5001.14
Q3-4745.242
95-th percentile-4015.33
Maximum394.56
Range19014
Interquartile range (IQR)720.148

Descriptive statistics

Standard deviation1545.7363
Coefficient of variation (CV)-0.28944564
Kurtosis18.14172
Mean-5340.3338
Median Absolute Deviation (MAD)308.67
Skewness-3.2730232
Sum-5340333.8
Variance2389300.8
MonotonicityNot monotonic
2023-01-11T12:07:47.151365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4522.64 2
 
0.2%
-5239.84 2
 
0.2%
-5043.24 2
 
0.2%
-4957.24 2
 
0.2%
-5013.44 2
 
0.2%
-5011.24 2
 
0.2%
-5366.04 2
 
0.2%
-5316.64 2
 
0.2%
-6891.44 2
 
0.2%
-4986.04 2
 
0.2%
Other values (970) 980
98.0%
ValueCountFrequency (%)
-18619.44 1
0.1%
-16711.44 1
0.1%
-16149.44 1
0.1%
-15835.44 1
0.1%
-14119.44 1
0.1%
-13759.44 1
0.1%
-13359.44 1
0.1%
-13273.44 1
0.1%
-12475.44 1
0.1%
-12227.44 1
0.1%
ValueCountFrequency (%)
394.56 1
0.1%
-1163.44 1
0.1%
-1195.44 1
0.1%
-1327.44 1
0.1%
-1415.44 1
0.1%
-1727.44 1
0.1%
-1813.44 1
0.1%
-1859.44 1
0.1%
-1909.44 1
0.1%
-2153.44 1
0.1%

F4
Real number (ℝ)

Distinct955
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-13.264612
Minimum-24.168
Maximum-10.53456
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:07:47.211701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-24.168
5-th percentile-18.8742
Q1-14.34075
median-12.50715
Q3-11.3808
95-th percentile-10.69845
Maximum-10.53456
Range13.63344
Interquartile range (IQR)2.95995

Descriptive statistics

Standard deviation2.5264282
Coefficient of variation (CV)-0.19046378
Kurtosis1.8661342
Mean-13.264612
Median Absolute Deviation (MAD)1.32525
Skewness-1.4311635
Sum-13264.612
Variance6.3828393
MonotonicityNot monotonic
2023-01-11T12:07:47.272387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11.2713 3
 
0.3%
-14.508 3
 
0.3%
-10.9371 2
 
0.2%
-16.995 2
 
0.2%
-14.052 2
 
0.2%
-13.722 2
 
0.2%
-12.0342 2
 
0.2%
-12.4596 2
 
0.2%
-11.574 2
 
0.2%
-14.673 2
 
0.2%
Other values (945) 978
97.8%
ValueCountFrequency (%)
-24.168 1
0.1%
-23.286 1
0.1%
-23.259 1
0.1%
-23.04 1
0.1%
-22.734 1
0.1%
-22.518 1
0.1%
-22.002 1
0.1%
-21.855 1
0.1%
-21.492 1
0.1%
-21.381 1
0.1%
ValueCountFrequency (%)
-10.53456 1
0.1%
-10.53609 1
0.1%
-10.5399 1
0.1%
-10.54104 1
0.1%
-10.54704 1
0.1%
-10.54893 1
0.1%
-10.55298 1
0.1%
-10.55349 1
0.1%
-10.55355 1
0.1%
-10.56861 1
0.1%

F5
Real number (ℝ)

Distinct941
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.308243
Minimum-13.593
Maximum-3.993498
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:07:47.333350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-13.593
5-th percentile-9.79035
Q1-7.38075
median-5.8815
Q3-4.863375
95-th percentile-4.165536
Maximum-3.993498
Range9.599502
Interquartile range (IQR)2.517375

Descriptive statistics

Standard deviation1.776885
Coefficient of variation (CV)-0.28167669
Kurtosis0.64213197
Mean-6.308243
Median Absolute Deviation (MAD)1.1829
Skewness-0.95673779
Sum-6308.243
Variance3.1573204
MonotonicityNot monotonic
2023-01-11T12:07:47.393560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.875 3
 
0.3%
-7.335 3
 
0.3%
-4.3572 3
 
0.3%
-7.494 3
 
0.3%
-5.4156 2
 
0.2%
-8.589 2
 
0.2%
-7.386 2
 
0.2%
-4.7757 2
 
0.2%
-7.104 2
 
0.2%
-7.644 2
 
0.2%
Other values (931) 976
97.6%
ValueCountFrequency (%)
-13.593 1
0.1%
-13.416 1
0.1%
-12.684 1
0.1%
-12.435 1
0.1%
-12.177 1
0.1%
-11.913 1
0.1%
-11.643 1
0.1%
-11.559 1
0.1%
-11.553 1
0.1%
-11.454 1
0.1%
ValueCountFrequency (%)
-3.993498 1
0.1%
-4.009221 1
0.1%
-4.010682 1
0.1%
-4.015149 1
0.1%
-4.02435 1
0.1%
-4.02771 1
0.1%
-4.03278 1
0.1%
-4.03527 1
0.1%
-4.03566 1
0.1%
-4.0365 1
0.1%

F6
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
515 
0
485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 515
51.5%
0 485
48.5%

Length

2023-01-11T12:07:47.445537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T12:07:47.488446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 515
51.5%
0 485
48.5%

Most occurring characters

ValueCountFrequency (%)
1 515
51.5%
0 485
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 515
51.5%
0 485
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 515
51.5%
0 485
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 515
51.5%
0 485
48.5%

F7
Real number (ℝ)

Distinct961
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8676394
Minimum3.94266
Maximum12.712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:07:47.533219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.94266
5-th percentile4.05032
Q14.5011
median5.2897
Q36.663
95-th percentile9.671
Maximum12.712
Range8.76934
Interquartile range (IQR)2.1619

Descriptive statistics

Standard deviation1.7749821
Coefficient of variation (CV)0.30250361
Kurtosis1.3416236
Mean5.8676394
Median Absolute Deviation (MAD)0.9551
Skewness1.3297723
Sum5867.6394
Variance3.1505615
MonotonicityNot monotonic
2023-01-11T12:07:47.591071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.98 3
 
0.3%
6.358 3
 
0.3%
6.794 2
 
0.2%
4.9848 2
 
0.2%
6.458 2
 
0.2%
4.1594 2
 
0.2%
6.252 2
 
0.2%
5.908 2
 
0.2%
7.244 2
 
0.2%
5.3268 2
 
0.2%
Other values (951) 978
97.8%
ValueCountFrequency (%)
3.94266 1
0.1%
3.94484 1
0.1%
3.94736 1
0.1%
3.95024 1
0.1%
3.95056 1
0.1%
3.95124 1
0.1%
3.95232 1
0.1%
3.954 1
0.1%
3.95418 1
0.1%
3.95468 1
0.1%
ValueCountFrequency (%)
12.712 1
0.1%
12.368 1
0.1%
12.278 1
0.1%
12.276 1
0.1%
12.274 1
0.1%
12.194 1
0.1%
12.05 1
0.1%
11.66 1
0.1%
11.6 1
0.1%
11.542 1
0.1%

F8
Real number (ℝ)

Distinct974
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-13.701405
Minimum-29.77
Maximum3.43
Zeros0
Zeros (%)0.0%
Negative996
Negative (%)99.6%
Memory size7.9 KiB
2023-01-11T12:07:47.649656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-29.77
5-th percentile-22.2564
Q1-17.3005
median-13.62295
Q3-10.1585
95-th percentile-4.93635
Maximum3.43
Range33.2
Interquartile range (IQR)7.142

Descriptive statistics

Standard deviation5.2142544
Coefficient of variation (CV)-0.38056348
Kurtosis-0.061502656
Mean-13.701405
Median Absolute Deviation (MAD)3.576
Skewness0.020337166
Sum-13701.405
Variance27.188449
MonotonicityNot monotonic
2023-01-11T12:07:47.706422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-15.598 3
 
0.3%
-11.237 2
 
0.2%
-12.435 2
 
0.2%
-14.949 2
 
0.2%
-9.454 2
 
0.2%
-12.269 2
 
0.2%
-12.9804 2
 
0.2%
-12.367 2
 
0.2%
-17.405 2
 
0.2%
-16.961 2
 
0.2%
Other values (964) 979
97.9%
ValueCountFrequency (%)
-29.77 1
0.1%
-27.59 1
0.1%
-27.48 1
0.1%
-26.61 1
0.1%
-26.4 1
0.1%
-26.35 1
0.1%
-26.28 1
0.1%
-26.24 1
0.1%
-26.23 1
0.1%
-26.2 1
0.1%
ValueCountFrequency (%)
3.43 1
0.1%
1.29 1
0.1%
0.84 1
0.1%
0.23 1
0.1%
-0.06 1
0.1%
-0.42 1
0.1%
-0.5 1
0.1%
-1.14 1
0.1%
-1.56 1
0.1%
-1.61 1
0.1%

F9
Real number (ℝ)

Distinct946
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-19.974703
Minimum-136.42
Maximum-0.130082
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:07:47.767299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-136.42
5-th percentile-58.461
Q1-27.285
median-13.59
Q3-5.87
95-th percentile-1.23089
Maximum-0.130082
Range136.28992
Interquartile range (IQR)21.415

Descriptive statistics

Standard deviation19.792541
Coefficient of variation (CV)-0.99088034
Kurtosis4.6306849
Mean-19.974703
Median Absolute Deviation (MAD)8.994
Skewness-1.9126264
Sum-19974.703
Variance391.74466
MonotonicityNot monotonic
2023-01-11T12:07:47.829409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.2 3
 
0.3%
-26.5 3
 
0.3%
-12.378 2
 
0.2%
-3.408 2
 
0.2%
-5.052 2
 
0.2%
-2.906 2
 
0.2%
-4.116 2
 
0.2%
-48.6 2
 
0.2%
-46.82 2
 
0.2%
-72.66 2
 
0.2%
Other values (936) 978
97.8%
ValueCountFrequency (%)
-136.42 1
0.1%
-117.54 1
0.1%
-114.8 1
0.1%
-108.52 1
0.1%
-106.12 1
0.1%
-104.22 1
0.1%
-103.6 1
0.1%
-99.9 1
0.1%
-95.44 1
0.1%
-94.18 1
0.1%
ValueCountFrequency (%)
-0.130082 1
0.1%
-0.15318 1
0.1%
-0.16628 1
0.1%
-0.1925 1
0.1%
-0.21986 1
0.1%
-0.26036 1
0.1%
-0.28172 1
0.1%
-0.30098 1
0.1%
-0.3252 1
0.1%
-0.3864 1
0.1%

F10
Real number (ℝ)

Distinct952
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9462801
Minimum3.00342
Maximum7.555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:07:47.889413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.00342
5-th percentile3.05149
Q13.276925
median3.64255
Q34.38925
95-th percentile5.82965
Maximum7.555
Range4.55158
Interquartile range (IQR)1.112325

Descriptive statistics

Standard deviation0.89226133
Coefficient of variation (CV)0.22610188
Kurtosis1.7990544
Mean3.9462801
Median Absolute Deviation (MAD)0.4316
Skewness1.4211338
Sum3946.2801
Variance0.79613028
MonotonicityNot monotonic
2023-01-11T12:07:47.946396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.151 3
 
0.3%
4.163 3
 
0.3%
3.953 3
 
0.3%
3.4114 2
 
0.2%
3.6094 2
 
0.2%
5.463 2
 
0.2%
4.473 2
 
0.2%
3.0876 2
 
0.2%
3.2167 2
 
0.2%
4.272 2
 
0.2%
Other values (942) 977
97.7%
ValueCountFrequency (%)
3.00342 1
0.1%
3.00388 1
0.1%
3.00582 1
0.1%
3.00618 1
0.1%
3.00677 1
0.1%
3.00714 1
0.1%
3.00715 1
0.1%
3.0073 1
0.1%
3.00855 1
0.1%
3.00925 1
0.1%
ValueCountFrequency (%)
7.555 1
0.1%
7.542 1
0.1%
7.377 1
0.1%
7.359 1
0.1%
7.266 1
0.1%
7.216 1
0.1%
7.193 1
0.1%
7.166 1
0.1%
7.102 1
0.1%
7.094 1
0.1%

F11
Real number (ℝ)

Distinct978
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2670.6858
Minimum-4674.3
Maximum503.7
Zeros0
Zeros (%)0.0%
Negative999
Negative (%)99.9%
Memory size7.9 KiB
2023-01-11T12:07:48.001539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4674.3
5-th percentile-3042.585
Q1-2862.56
median-2783.065
Q3-2616.5
95-th percentile-1884.495
Maximum503.7
Range5178
Interquartile range (IQR)246.06

Descriptive statistics

Standard deviation446.4814
Coefficient of variation (CV)-0.16717856
Kurtosis11.336581
Mean-2670.6858
Median Absolute Deviation (MAD)100.595
Skewness2.6061769
Sum-2670685.8
Variance199345.64
MonotonicityNot monotonic
2023-01-11T12:07:48.058264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2659.2 3
 
0.3%
-2849.1 2
 
0.2%
-2859.07 2
 
0.2%
-2701.5 2
 
0.2%
-2732.3 2
 
0.2%
-2889.86 2
 
0.2%
-2727.6 2
 
0.2%
-2651.2 2
 
0.2%
-2595.3 2
 
0.2%
-2722.6 2
 
0.2%
Other values (968) 979
97.9%
ValueCountFrequency (%)
-4674.3 1
0.1%
-4066.3 1
0.1%
-3956.3 1
0.1%
-3838.3 1
0.1%
-3831.5 1
0.1%
-3772.1 1
0.1%
-3685 1
0.1%
-3571.5 1
0.1%
-3546.3 1
0.1%
-3482.7 1
0.1%
ValueCountFrequency (%)
503.7 1
0.1%
-102.3 1
0.1%
-229.3 1
0.1%
-368.3 1
0.1%
-387.3 1
0.1%
-486.3 1
0.1%
-538.3 1
0.1%
-570.3 1
0.1%
-580.3 1
0.1%
-595.3 1
0.1%

F12
Real number (ℝ)

Distinct930
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.5283126
Minimum-10.744
Maximum-1.64318
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:07:48.116514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-10.744
5-th percentile-7.0392
Q1-4.398
median-3.0084
Q3-2.20075
95-th percentile-1.725456
Maximum-1.64318
Range9.10082
Interquartile range (IQR)2.19725

Descriptive statistics

Standard deviation1.7354655
Coefficient of variation (CV)-0.49186839
Kurtosis1.7590447
Mean-3.5283126
Median Absolute Deviation (MAD)0.96
Skewness-1.3725463
Sum-3528.3126
Variance3.0118403
MonotonicityNot monotonic
2023-01-11T12:07:48.261032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.982 3
 
0.3%
-6.18 3
 
0.3%
-2.2724 3
 
0.3%
-3.766 3
 
0.3%
-3.922 3
 
0.3%
-5.238 2
 
0.2%
-4.476 2
 
0.2%
-5.024 2
 
0.2%
-4.658 2
 
0.2%
-6.084 2
 
0.2%
Other values (920) 975
97.5%
ValueCountFrequency (%)
-10.744 1
0.1%
-10.626 1
0.1%
-10.464 1
0.1%
-9.942 1
0.1%
-9.918 1
0.1%
-9.81 1
0.1%
-9.598 1
0.1%
-9.53 1
0.1%
-9.43 1
0.1%
-9.404 1
0.1%
ValueCountFrequency (%)
-1.64318 2
0.2%
-1.64476 1
0.1%
-1.64618 1
0.1%
-1.64924 1
0.1%
-1.65034 1
0.1%
-1.65158 1
0.1%
-1.65736 1
0.1%
-1.6596 1
0.1%
-1.66304 1
0.1%
-1.66334 1
0.1%

F13
Real number (ℝ)

Distinct976
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2525208
Minimum5.7600023
Maximum12.152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:07:48.318663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum5.7600023
5-th percentile5.7618528
Q15.8105325
median5.97975
Q36.40035
95-th percentile7.6191
Maximum12.152
Range6.3919977
Interquartile range (IQR)0.5898175

Descriptive statistics

Standard deviation0.72625262
Coefficient of variation (CV)0.11615357
Kurtosis13.51556
Mean6.2525208
Median Absolute Deviation (MAD)0.20236
Skewness3.0609482
Sum6252.5208
Variance0.52744287
MonotonicityNot monotonic
2023-01-11T12:07:48.375508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.78526 2
 
0.2%
5.8787 2
 
0.2%
5.9999 2
 
0.2%
5.9194 2
 
0.2%
7.064 2
 
0.2%
5.8944 2
 
0.2%
5.922 2
 
0.2%
7.089 2
 
0.2%
5.908 2
 
0.2%
6.0502 2
 
0.2%
Other values (966) 980
98.0%
ValueCountFrequency (%)
5.7600023 1
0.1%
5.7600027 1
0.1%
5.7600043 1
0.1%
5.7600053 1
0.1%
5.7600055 2
0.2%
5.7600065 1
0.1%
5.7600222 1
0.1%
5.7600292 1
0.1%
5.7600297 1
0.1%
5.7600331 1
0.1%
ValueCountFrequency (%)
12.152 1
0.1%
11.839 1
0.1%
10.885 1
0.1%
10.458 1
0.1%
9.627 1
0.1%
9.554 1
0.1%
9.399 1
0.1%
9.376 1
0.1%
9.339 1
0.1%
9.298 1
0.1%

F14
Real number (ℝ)

Distinct975
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11636.477
Minimum-19748.76
Maximum-1151.76
Zeros0
Zeros (%)0.0%
Negative1000
Negative (%)100.0%
Memory size7.9 KiB
2023-01-11T12:07:48.437236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-19748.76
5-th percentile-13900.215
Q1-12024.735
median-11618.2
Q3-11189.16
95-th percentile-9300.03
Maximum-1151.76
Range18597
Interquartile range (IQR)835.575

Descriptive statistics

Standard deviation1506.0916
Coefficient of variation (CV)-0.12942849
Kurtosis8.4713641
Mean-11636.477
Median Absolute Deviation (MAD)416.8095
Skewness-0.041243454
Sum-11636477
Variance2268311.9
MonotonicityNot monotonic
2023-01-11T12:07:48.494554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10775.76 2
 
0.2%
-12014.46 2
 
0.2%
-11697.99 2
 
0.2%
-11189.16 2
 
0.2%
-12801.96 2
 
0.2%
-11171.46 2
 
0.2%
-12328.26 2
 
0.2%
-13406.46 2
 
0.2%
-12479.16 2
 
0.2%
-11222.46 2
 
0.2%
Other values (965) 980
98.0%
ValueCountFrequency (%)
-19748.76 1
0.1%
-19589.76 1
0.1%
-19016.76 1
0.1%
-18221.76 1
0.1%
-18185.76 1
0.1%
-17897.76 1
0.1%
-17741.76 1
0.1%
-17414.76 1
0.1%
-16913.76 1
0.1%
-16826.76 1
0.1%
ValueCountFrequency (%)
-1151.76 1
0.1%
-4070.76 1
0.1%
-5162.76 1
0.1%
-5249.76 1
0.1%
-5363.76 1
0.1%
-5783.76 1
0.1%
-6119.76 1
0.1%
-6350.76 1
0.1%
-6587.76 1
0.1%
-6632.76 1
0.1%

F15
Real number (ℝ)

Distinct972
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24081.766
Minimum6332.64
Maximum43343.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:07:48.556088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6332.64
5-th percentile21334.17
Q122928.873
median23498.94
Q324481.89
95-th percentile29376.54
Maximum43343.64
Range37011
Interquartile range (IQR)1553.0175

Descriptive statistics

Standard deviation2999.6916
Coefficient of variation (CV)0.12456278
Kurtosis11.870644
Mean24081.766
Median Absolute Deviation (MAD)700.05
Skewness1.656686
Sum24081766
Variance8998149.9
MonotonicityNot monotonic
2023-01-11T12:07:48.615072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23790.24 3
 
0.3%
28475.64 3
 
0.3%
23558.64 2
 
0.2%
23709.84 2
 
0.2%
30185.64 2
 
0.2%
24734.04 2
 
0.2%
23611.74 2
 
0.2%
23507.94 2
 
0.2%
29222.64 2
 
0.2%
23627.34 2
 
0.2%
Other values (962) 978
97.8%
ValueCountFrequency (%)
6332.64 1
0.1%
8312.64 1
0.1%
8942.64 1
0.1%
10511.64 1
0.1%
12308.64 1
0.1%
12437.64 1
0.1%
16802.64 1
0.1%
17780.64 1
0.1%
18035.64 1
0.1%
18062.64 1
0.1%
ValueCountFrequency (%)
43343.64 1
0.1%
42368.64 1
0.1%
40589.64 1
0.1%
40586.64 1
0.1%
39614.64 1
0.1%
38987.64 1
0.1%
38588.64 1
0.1%
37079.64 1
0.1%
36917.64 1
0.1%
36908.64 1
0.1%

F16
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
-0.4
500 
-1.4
500 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4
2nd row-1.4
3rd row-0.4
4th row-0.4
5th row-1.4

Common Values

ValueCountFrequency (%)
-0.4 500
50.0%
-1.4 500
50.0%

Length

2023-01-11T12:07:48.670271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T12:07:48.712778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.4 500
50.0%
1.4 500
50.0%

Most occurring characters

ValueCountFrequency (%)
- 1000
25.0%
. 1000
25.0%
4 1000
25.0%
0 500
12.5%
1 500
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
50.0%
Dash Punctuation 1000
25.0%
Other Punctuation 1000
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 1000
50.0%
0 500
25.0%
1 500
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1000
25.0%
. 1000
25.0%
4 1000
25.0%
0 500
12.5%
1 500
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000
25.0%
. 1000
25.0%
4 1000
25.0%
0 500
12.5%
1 500
12.5%

F17
Real number (ℝ)

Distinct964
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103818.02
Minimum94174.66
Maximum112720.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:07:48.757691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum94174.66
5-th percentile103563.63
Q1103813.21
median103849.54
Q3103888.61
95-th percentile104071.36
Maximum112720.66
Range18546
Interquartile range (IQR)75.405

Descriptive statistics

Standard deviation664.88437
Coefficient of variation (CV)0.0064043256
Kurtosis124.94736
Mean103818.02
Median Absolute Deviation (MAD)37.955
Skewness-4.8188525
Sum1.0381802 × 108
Variance442071.23
MonotonicityNot monotonic
2023-01-11T12:07:48.815900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103856.66 3
 
0.3%
103848.47 2
 
0.2%
103775.62 2
 
0.2%
103884.66 2
 
0.2%
103867.19 2
 
0.2%
103835.9 2
 
0.2%
103872.48 2
 
0.2%
103858.17 2
 
0.2%
103835.69 2
 
0.2%
103852.3 2
 
0.2%
Other values (954) 979
97.9%
ValueCountFrequency (%)
94174.66 1
0.1%
94892.66 1
0.1%
97180.66 1
0.1%
99060.66 1
0.1%
99544.66 1
0.1%
100012.66 1
0.1%
100660.66 1
0.1%
101204.66 1
0.1%
101308.66 1
0.1%
102073.66 1
0.1%
ValueCountFrequency (%)
112720.66 1
0.1%
107960.66 1
0.1%
106002.66 1
0.1%
105597.86 1
0.1%
105495.46 1
0.1%
105361.06 1
0.1%
105133.86 1
0.1%
105041.46 1
0.1%
104902.46 1
0.1%
104852.06 1
0.1%

F18
Real number (ℝ)

Distinct950
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0016761
Minimum2.14332
Maximum11.058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2023-01-11T12:07:48.879749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.14332
5-th percentile2.239737
Q12.7029
median3.4498
Q34.923
95-th percentile7.5434
Maximum11.058
Range8.91468
Interquartile range (IQR)2.2201

Descriptive statistics

Standard deviation1.7296722
Coefficient of variation (CV)0.43223694
Kurtosis1.9661959
Mean4.0016761
Median Absolute Deviation (MAD)0.9237
Skewness1.4309999
Sum4001.6761
Variance2.991766
MonotonicityNot monotonic
2023-01-11T12:07:48.934505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7874 3
 
0.3%
5.58 3
 
0.3%
5.082 3
 
0.3%
2.7782 2
 
0.2%
5.818 2
 
0.2%
4.416 2
 
0.2%
4.05 2
 
0.2%
5.024 2
 
0.2%
2.6648 2
 
0.2%
4.622 2
 
0.2%
Other values (940) 977
97.7%
ValueCountFrequency (%)
2.14332 1
0.1%
2.14352 1
0.1%
2.14356 1
0.1%
2.14474 1
0.1%
2.14576 1
0.1%
2.15802 1
0.1%
2.16062 1
0.1%
2.16388 1
0.1%
2.1647 1
0.1%
2.1654 1
0.1%
ValueCountFrequency (%)
11.058 1
0.1%
10.88 1
0.1%
10.724 1
0.1%
10.356 1
0.1%
10.322 1
0.1%
10.294 1
0.1%
10.166 1
0.1%
10.016 1
0.1%
9.99 1
0.1%
9.904 1
0.1%

F19
Real number (ℝ)

Distinct977
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1474.5324
Minimum-456.08
Maximum4337.92
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.4%
Memory size7.9 KiB
2023-01-11T12:07:48.989306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-456.08
5-th percentile1143.69
Q11441.89
median1505.215
Q31542.3875
95-th percentile1692.815
Maximum4337.92
Range4794
Interquartile range (IQR)100.4975

Descriptive statistics

Standard deviation234.67574
Coefficient of variation (CV)0.15915265
Kurtosis35.960396
Mean1474.5324
Median Absolute Deviation (MAD)47.1
Skewness-0.28329472
Sum1474532.4
Variance55072.705
MonotonicityNot monotonic
2023-01-11T12:07:49.045020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1519.1 2
 
0.2%
1429.12 2
 
0.2%
1476.36 2
 
0.2%
1141.22 2
 
0.2%
1332.02 2
 
0.2%
1412.02 2
 
0.2%
1408.72 2
 
0.2%
1426.12 2
 
0.2%
1473.56 2
 
0.2%
1427.72 2
 
0.2%
Other values (967) 980
98.0%
ValueCountFrequency (%)
-456.08 1
0.1%
-172.08 1
0.1%
-100.08 1
0.1%
-95.08 1
0.1%
192.92 1
0.1%
297.92 1
0.1%
467.92 1
0.1%
542.82 1
0.1%
556.12 1
0.1%
612.82 1
0.1%
ValueCountFrequency (%)
4337.92 1
0.1%
2687.92 1
0.1%
2247.82 1
0.1%
2185.72 1
0.1%
2157.92 1
0.1%
2138.02 1
0.1%
2128.82 1
0.1%
2090.92 1
0.1%
2083.22 1
0.1%
2076.92 1
0.1%

F20
Real number (ℝ)

Distinct979
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3983.342
Minimum-14687.48
Maximum7362.52
Zeros0
Zeros (%)0.0%
Negative976
Negative (%)97.6%
Memory size7.9 KiB
2023-01-11T12:07:49.103659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-14687.48
5-th percentile-5388.06
Q1-4524.09
median-4226.07
Q3-3709.88
95-th percentile-1823.08
Maximum7362.52
Range22050
Interquartile range (IQR)814.21

Descriptive statistics

Standard deviation1444.5331
Coefficient of variation (CV)-0.36264351
Kurtosis16.645003
Mean-3983.342
Median Absolute Deviation (MAD)366.21
Skewness1.9090319
Sum-3983342
Variance2086675.9
MonotonicityNot monotonic
2023-01-11T12:07:49.159708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4647.88 2
 
0.2%
-4744.28 2
 
0.2%
-4934.68 2
 
0.2%
-3565.88 2
 
0.2%
-4917.88 2
 
0.2%
-4103.68 2
 
0.2%
-4644.08 2
 
0.2%
-4079.68 2
 
0.2%
-4627.28 2
 
0.2%
-4644.48 2
 
0.2%
Other values (969) 980
98.0%
ValueCountFrequency (%)
-14687.48 1
0.1%
-10643.48 1
0.1%
-10127.48 1
0.1%
-8997.48 1
0.1%
-8799.48 1
0.1%
-8441.48 1
0.1%
-7835.48 1
0.1%
-7365.48 1
0.1%
-7339.48 1
0.1%
-7163.48 1
0.1%
ValueCountFrequency (%)
7362.52 1
0.1%
6326.52 1
0.1%
5720.52 1
0.1%
4724.52 1
0.1%
3438.52 1
0.1%
2984.52 1
0.1%
2952.52 1
0.1%
2436.52 1
0.1%
2206.52 1
0.1%
1810.52 1
0.1%

F21
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct278
Distinct (%)55.6%
Missing500
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean-44.68914
Minimum-53.52
Maximum-31.47
Zeros0
Zeros (%)0.0%
Negative500
Negative (%)50.0%
Memory size7.9 KiB
2023-01-11T12:07:49.217460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-53.52
5-th percentile-49.686
Q1-46.86
median-44.76
Q3-42.4725
95-th percentile-39.5385
Maximum-31.47
Range22.05
Interquartile range (IQR)4.3875

Descriptive statistics

Standard deviation3.1618325
Coefficient of variation (CV)-0.070751697
Kurtosis0.035770391
Mean-44.68914
Median Absolute Deviation (MAD)2.19
Skewness0.1225613
Sum-22344.57
Variance9.9971846
MonotonicityNot monotonic
2023-01-11T12:07:49.271482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-45 6
 
0.6%
-47.07 5
 
0.5%
-45.81 5
 
0.5%
-45.48 5
 
0.5%
-43.92 5
 
0.5%
-43.56 5
 
0.5%
-48.81 5
 
0.5%
-46.86 5
 
0.5%
-45.78 5
 
0.5%
-42.78 4
 
0.4%
Other values (268) 450
45.0%
(Missing) 500
50.0%
ValueCountFrequency (%)
-53.52 1
 
0.1%
-52.56 1
 
0.1%
-52.32 1
 
0.1%
-52.26 1
 
0.1%
-51.33 3
0.3%
-51.27 1
 
0.1%
-51.18 1
 
0.1%
-51.15 1
 
0.1%
-51 1
 
0.1%
-50.85 1
 
0.1%
ValueCountFrequency (%)
-31.47 1
 
0.1%
-36.18 1
 
0.1%
-37.23 1
 
0.1%
-37.35 1
 
0.1%
-37.59 1
 
0.1%
-38.28 1
 
0.1%
-38.4 1
 
0.1%
-38.49 1
 
0.1%
-38.58 1
 
0.1%
-38.61 3
0.3%

Class
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing1000
Missing (%)100.0%
Memory size7.9 KiB

Interactions

2023-01-11T12:07:45.749503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.344060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.372782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.343550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.393297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.315892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.212188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.251426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.203781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.100397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.095936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.996443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.928857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.987536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.978426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.951087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.941875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.842290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.790054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.424190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.422662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.393933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.441391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.364758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.263134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.301585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.251000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.148662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.144873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.046165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.979719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.039389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.031627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.999859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.989740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.891020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.837640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.513063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.479619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.451042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.497156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.419778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.320069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.357240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.303685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.202337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.197844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.102767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.036907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.097844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.088732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.052800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.044183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.945276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.882950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.628094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.537632image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.507686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.551034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.472315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.376170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.414346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.357381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.258125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.252092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.158332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.093301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.156527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.146352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.107255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.097109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.999284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.927409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.677694image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.590902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.559018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.601404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.521576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.429963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.467134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.406858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.308897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.301713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.209700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.147968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.210818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.201541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.155597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.146670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.047859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.967964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.725550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.642630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.610430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.649478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.568785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.480015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.517625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.454224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.357666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.350505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.258277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.199299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.264091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.254058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.205167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.195468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.096298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.013839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.779170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.700935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.666165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.704026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.622562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.535850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.572563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.506420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.411908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.403176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.313941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.257351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.322689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.310871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.257718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.249013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.149748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.058289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.831899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.757998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.812883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.757699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.674662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.591505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.628845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.560808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.465179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.456368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.368672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.313762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.380892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.368322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.311457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.301535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.203746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.099755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.880443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.810811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.864068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.806351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.721618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.728954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.680526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.606942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.513759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.502939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.419045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.367303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.433856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.421119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.358425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.348821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.251390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.141003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.928225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.863494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.915504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.855508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.768762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.778906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.731893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.656185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.561232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.551333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.468164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.418710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.488135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.473439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.407012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.396148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.299693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.181614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:29.976283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.915350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.967557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.905048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.816118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.830629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.781827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.702244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.608451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.597633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.518283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.471013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.540212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.526448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.453863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.445024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.347859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.227670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.026287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.970603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.023649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.957434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.866758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.884419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.835759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.753195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.749496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.647392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.571156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.526431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.597627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.581782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.505837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.496260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.400209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.273058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.080215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.026232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.080707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.014030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.920644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.941565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.891911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.806518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.803288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.702990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.626633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.583112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.656198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.639637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.559946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.551545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.455262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.322810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.135032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.086786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.139547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.071198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.975547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.999017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.950536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.862977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.858680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.758399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.683446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.642815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.715927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.699295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.616695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.607159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.510963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.363536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.188960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.144631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.196889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.127805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.029621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.056098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.008441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.916075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.913663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.813221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.738778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.700713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.774105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.755975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.670095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.660642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.563965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.404595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.237026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.197546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.248391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.176205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.076215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.107143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.059755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.965453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.960465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.860139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.787356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.751877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.828855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.807445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.717181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.707981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.611896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.446341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.284260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.248336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.299550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.226481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.125177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.157561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.110396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.012546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.008528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.907884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.837137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.893034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.880911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.859954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.855068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.755021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.660988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:46.486655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:30.331930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:31.299782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:32.350285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:33.274394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:34.172171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:35.208127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:36.161723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:37.059264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.056340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:38.955990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:39.885984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:40.943817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:41.933404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:42.910371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:43.901599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:44.802269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T12:07:45.708701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-11T12:07:49.329502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-01-11T12:07:49.436999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-11T12:07:49.640447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-11T12:07:49.750557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-11T12:07:49.843439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-11T12:07:49.909507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-11T12:07:46.566227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-11T12:07:46.794026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

F1F2F3F4F5F6F7F8F9F10F11F12F13F14F15F16F17F18F19F20F21Class
01.56201-4522.64-10.78410-6.703815.4270-9.679-18.5660005.86600-3368.8000-1.847405.761109-11347.2322929.33-0.4103741.464.07601466.0700-4266.40NaNNaN
11.84001-3893.64-12.68850-7.983004.5008-11.561-25.9200003.18100-2838.2092-2.918806.797000-11289.3625723.74-1.4103858.012.65461607.6600-4802.48-44.91NaN
20.37821-4916.46-11.93910-5.113215.3808-13.281-20.2400003.04817-2562.5000-3.904006.922000-11133.0623138.58-0.4105361.065.63001543.2200-4220.46-45.66NaN
31.14100-8705.44-10.97370-6.956416.5020-12.101-13.6260003.28770-1907.8000-6.148009.260000-11773.5323100.78-0.4103835.752.36801532.0397-4612.88-43.26NaN
41.43601-6963.44-15.97800-10.242014.2970-11.596-14.6240003.55760-2739.5300-1.643185.997600-11937.0627299.64-1.4103877.642.69081084.3200-4557.08-44.82NaN
50.54941-5234.24-10.60332-5.801716.1560-11.400-1.7450003.06040-643.3000-2.753407.703000-12663.3622854.84-1.4103795.725.92001490.6400-5650.08-48.30NaN
60.64830-4873.00-11.34420-4.381804.1438-7.197-51.5600003.41470-2971.4000-6.502005.760033-12384.6631388.64-1.4103877.484.40401485.0800-3358.68NaNNaN
72.45900-6965.44-11.26290-8.652004.9574-20.824-56.9800005.41800-2970.1000-2.296605.832100-11756.9722994.61-1.4103877.222.54521485.0600-4438.76NaNNaN
80.48371-4708.72-10.82610-8.991009.6680-18.146-16.2080003.01154-2065.2000-2.620805.802190-10955.1623490.84-1.4103774.063.18341537.1280-1931.48-45.48NaN
90.97841-5049.44-13.80600-5.493908.3440-8.720-0.1300823.53010-2730.5000-6.076006.319000-11318.4623087.49-0.4103747.743.85461549.7900-4573.68-39.69NaN
F1F2F3F4F5F6F7F8F9F10F11F12F13F14F15F16F17F18F19F20F21Class
9900.334100-4852.14-10.7082-8.3910007.4020-10.704-0.59263.6319-3050.200-2.17465.816510-11426.43023911.74-0.4103858.355.81801666.82-5009.48NaNNaN
9910.190001-4676.34-13.1007-7.1100005.2656-15.670-8.71404.3770-2714.900-2.81665.993100-9566.76024504.24-0.4103840.154.13801509.63-4159.46NaNNaN
9920.209600-6965.44-14.2650-4.7694004.2194-16.035-106.12003.3679-2714.400-8.45205.868000-15974.76020876.34-1.4103847.382.78741503.23-3503.08NaNNaN
9930.733400-6475.04-18.4800-4.9410017.2440-26.100-8.36803.8175-2839.471-3.64805.979300-12303.06022937.13-0.4103522.462.73661472.20-4251.26NaNNaN
9940.997501-5228.24-12.7146-8.1960017.8860-11.717-11.29203.7055-2894.050-5.80805.763297-12084.06023705.94-0.4103996.983.01161487.72-3958.28NaNNaN
9950.421901-3638.64-15.7710-4.1659507.3160-19.894-73.36005.4590-2853.970-7.54405.778300-10418.16023734.14-1.4103839.182.91361518.39-4146.08NaNNaN
9960.123350-4821.32-13.9560-8.3400004.7972-7.204-79.42003.3793-2876.900-5.81807.407000-15800.76025006.44-0.4103840.6010.88001520.10-4665.08-45.93NaN
9970.117801-4677.82-11.5935-4.0243518.0940-12.809-1.91823.0642-2787.270-3.49445.960300-4070.76023033.76-0.4103993.709.69601493.18552.52-43.92NaN
9981.534000-4893.36-20.8680-4.8945015.1574-18.655-6.28803.9177-2867.640-3.05386.199500-11618.25722637.34-0.4103845.433.46101483.65-4203.62-44.46NaN
9991.332001-5190.64-11.3592-4.2732915.1350-21.093-15.74603.0527-2036.700-3.36485.768134-11306.76024056.04-1.4103801.563.43361617.32-6953.48NaNNaN